


import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class     A__   (       snake_case__      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   ):
         super().__init__()
         snake_case											=							value_function
         snake_case											=							unet
         snake_case											=							scheduler
         snake_case											=							env
         snake_case											=							env.get_dataset()
         snake_case											=							{}
         for key in self.data.keys():
              try:
                   snake_case											=							self.data[key].mean()
              except:  # noqa: E722
                   pass
         snake_case											=							{}
         for key in self.data.keys():
              try:
                   snake_case											=							self.data[key].std()
              except:  # noqa: E722
                   pass
         snake_case											=							env.observation_space.shape[0]
         snake_case											=							env.action_space.shape[0]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         return (x_in - self.means[key]) / self.stds[key]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         return x_in * self.stds[key] + self.means[key]
    def 						a_     (			self     ,   __snake_case       ):
         if type(__snake_case       ) is dict:
              return {k: self.to_torch(__snake_case       ) for k, v in x_in.items()}
         elif torch.is_tensor(__snake_case       ):
              return x_in.to(self.unet.device       )
         return torch.tensor(__snake_case     ,   device=self.unet.device       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         for key, val in cond.items():
              snake_case											=							val.clone()
         return x_in
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							x.shape[0]
         snake_case											=							None
         for i in tqdm.tqdm(self.scheduler.timesteps       ):
              # create batch of timesteps to pass into model
              snake_case											=							torch.full((batch_size,)     ,   __snake_case     ,   device=self.unet.device     ,   dtype=torch.long       )
              for _ in range(__snake_case       ):
                   with torch.enable_grad():
                        x.requires_grad_()

                        # permute to match dimension for pre-trained models
                        snake_case											=							self.value_function(x.permute(0     ,   2     ,   1       )     ,   __snake_case       ).sample
                        snake_case											=							torch.autograd.grad([y.sum()]     ,   [x]       )[0]

                        snake_case											=							self.scheduler._get_variance(__snake_case       )
                        snake_case											=							torch.exp(0.5 * posterior_variance       )
                        snake_case											=							model_std * grad

                   snake_case											=							0
                   snake_case											=							x.detach()
                   snake_case											=							x + scale * grad
                   snake_case											=							self.reset_xa(__snake_case     ,   __snake_case     ,   self.action_dim       )

              snake_case											=							self.unet(x.permute(0     ,   2     ,   1       )     ,   __snake_case       ).sample.permute(0     ,   2     ,   1       )

              # TODO: verify deprecation of this kwarg
              snake_case											=							self.scheduler.step(__snake_case     ,   __snake_case     ,   __snake_case     ,   predict_epsilon=__snake_case       )['''prev_sample''']

              # apply conditions to the trajectory (set the initial state)
              snake_case											=							self.reset_xa(__snake_case     ,   __snake_case     ,   self.action_dim       )
              snake_case											=							self.to_torch(__snake_case       )
         return x, y




    def __call__(			self     ,   __snake_case     ,   __snake_case=6_4     ,   __snake_case=3_2     ,   __snake_case=2     ,   __snake_case=0.1       ):
         # normalize the observations and create  batch dimension
         snake_case											=							self.normalize(__snake_case     ,   '''observations'''       )
         snake_case											=							obs[None].repeat(__snake_case     ,   axis=0       )

         snake_case											=							{0: self.to_torch(__snake_case       )}
         snake_case											=							(batch_size, planning_horizon, self.state_dim + self.action_dim)

         # generate initial noise and apply our conditions (to make the trajectories start at current state)
         snake_case											=							randn_tensor(__snake_case     ,   device=self.unet.device       )
         snake_case											=							self.reset_xa(__snake_case     ,   __snake_case     ,   self.action_dim       )
         snake_case											=							self.to_torch(__snake_case       )

         # run the diffusion process
         snake_case					,    snake_case											=							self.run_diffusion(__snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       )

         # sort output trajectories by value
         snake_case											=							y.argsort(0     ,   descending=__snake_case       ).squeeze()
         snake_case											=							x[sorted_idx]
         snake_case											=							sorted_values[:, :, : self.action_dim]
         snake_case											=							actions.detach().cpu().numpy()
         snake_case											=							self.de_normalize(__snake_case     ,   key='''actions'''       )

         # select the action with the highest value
         if y is not None:
              snake_case											=							0
         else:
              # if we didn't run value guiding, select a random action
              snake_case											=							np.random.randint(0     ,   __snake_case       )

         snake_case											=							denorm_actions[selected_index, 0]
         return denorm_actions








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       ["SpeechEncoderDecoderModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Dict      =       ["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
 from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
 import sys

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




